Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits.

Pharmacogenetic effects of 'candidate gene complexes' on stroke in the GenHAT study.

OBJECTIVE: The aim of this study was to investigate whether there is a genotype-by-treatment interaction in patients experiencing stroke and treated with one of three antihypertensive drugs, that is chlorthalidone, amlodipine, or lisinopril. PARTICIPANTS AND METHODS: A population of 436 African Americans and 539 whites who had experienced stroke in the GenHAT study were genotyped for 768 single nucleotide polymorphisms (SNPs) in 280 candidate genes. To detect a genotype-by-treatment interaction, we used the Pearson's χ-test to assess whether the genotype frequencies differed at the single SNP level for the three drug treatment groups. From these single SNP analyses, we derived a summary statistic for the degree of association at the gene and gene complex levels. This was done by grouping SNPs using information on gene locations and defining gene complexes on the basis of protein-protein interactions. To assess the statistical significance of the observed test statistic, we derived an empirical P-value by simulating data under the null hypothesis. RESULTS: We found that, in patients who have experienced stroke, there is a significant genetic difference between hypertension drug treatment groups. In African Americans, SNP rs12143842 showed a significant association (P<0.001) with drug treatment. At the gene level, HNRNPA1P4 and NOS1AP in African Americans and PRICKLE1 and NINJ2 in non-Hispanic whites were significantly associated (P<0.01) with drug treatment, whereas none of the gene complexes tested showed significance. CONCLUSION: On the basis of the genetic differences between drug treatment groups, we conclude that there may be an interaction between certain genotypes and antihypertensive treatment in stroke patients. This needs to be replicated in other studies.